Visual tracking is essential for many applications such as computer vision, human-machine interfacing, and human-human interfacing. Computer vision is especially focused in security technologies such as visual surveillance, and audio and visual technologies such as analysis, classification, and editing of recorded images. Human-human interfacing includes teleconferencing and videotelephony. Accordingly, there have been many studies undertaken on visual tracking, with a number of those specifically addressing tracking accuracy and processing efficiency. A major approach to visual tracking is now based on a particle filter. The particle filter attracts attention as a time series analysis tool for systems with non-Gaussian noise, which the well known Kalman filter cannot deal with. The CONDENSATION algorithm (Conditional Density Propagation) is well known as a technique based on a particle filter (see, for example, non-patent documents 1-3).
The particle filter is a computation method for the approximation of the Bayesian filter, and represents the probability distribution of a target object by introducing a finite number of particles as target candidates. The probability distribution of the target object is used for time series estimations and predictions. The Condensation algorithm uses a particle filter to estimate the time variation of the probability distribution of the shape of a target object. More specifically, the contour of a candidate having the same shape as the target object is represented by a single particle. The existence probability distributions of the object in the parameter space are sequentially estimated by parameter transition based on motion models and observation for calculating the likelihood of the transition results.    [non-patent document No. 1] Contour tracking by stochastic propagation of conditional density, Michael Isard and Andrew Blake, Proc. European Conf. on Computer Vision, vol. 1, pp. 343-356, Cambridge UK (1996)    [non-patent document No. 2] CONDENSATION—conditional density propagation for visual tracking, Michael Isard and Andrew Blake, Int. J. Computer Vision, 29, 1, 5-28 (1998)    [non-patent document No. 3] Condensation: Unifying low-level and high-level tracking in a stochastic framework, Michael Isard and Andrew Blake, Proc 5th European Conf. Computer Vision, 1998